Odometry estimation suffers from the accumulative error drifts during long-term operations, due to the fundamental problems of path integration, i.e. the system error accumulate without effective restrictions.
To address this, graph-SLAM [1]} constructs a topological graph to represent camera poses or scene features as graph nodes, which are connected by edges (measured by sensors) to constrain the poses.
This graph-based formulation can be optimized to ensure the global consistency of graph nodes and edges, mitigating the possible errors on pose estimates and the inherent sensor measurement noise. A popular solver for global optimization is through Levenberg-Marquardt (LM) algorithm.
